Steve Taylor - President at University of Chicago

Steve Taylor

President at University of Chicago

Short Hills, New Jersey, United States

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Summary

👤

Senior

🎓

Top School

Steve Taylor is a quantitative technology consultant and president of Geodesic Solutions with 13 years of experience applying mathematics, statistics, and numerical optimization to finance and fintech problems. He designs and delivers end-to-end solutions—from model development and trading strategy optimization to runtime profiling and Python training—serving proprietary trading, family office, sell-side, and startup clients. An experienced educator and mentor, he has taught fintech and data science at multiple universities and guided graduate capstone projects on Bayesian VaR and corporate bond valuation. His open-source contributions to the widely used dlib library demonstrate practical expertise in numerical methods (adaptive Simpson integration) and higher-order statistics (skewness and kurtosis), reflecting a blend of rigorous research and production-focused engineering. Based in Short Hills, NJ, he combines PhD-level quantitative training with hands-on software development to translate complex theory into robust, deployable systems.

13 years of coding experience

11 years of employment as a software developer

Master of Science (MS) Physics, Master of Science (MS) Physics at Brigham Young University

PhD Quantitative Finance, PhD Quantitative Finance at Frankfurt School of Finance & Management

High School Diploma, High School Diploma at Middletown High School

Mathematics, Mathematics at Stony Brook University

English

Github Skills (12)

algorithm10

unit-testing10

statistics10

computer-vision10

algorithms10

machine-learning10

c-language10

cprogramming-language10

numerical-methods10

deeplearning-ai9

deep-learning9

python8

Programming languages (3)

TypeScriptC++Python

Github contributions (5)

davisking/dlib

Mar 2013 - May 2013

A toolkit for making real world machine learning and data analysis applications in C++

Role in this project:

Back-end Developer & Data Scientist

Contributions:7 commits in 2 months

Contributions summary:Steve primarily contributed to the dlib library by implementing numerical methods and adding unit tests. Their work focused on extending the `running_stats` object with functions to calculate skewness and kurtosis, crucial for statistical analysis. They also added an adaptive Simpson rule for numerical integration, further expanding the library's capabilities for data analysis and scientific computing. These contributions demonstrate a focus on numerical methods and statistical analysis, directly aligned with the library's machine learning and data analysis focus.

data-analysispythondata-sciencedeep-learningc-plus-plus

steve98654/website

May 2017 - Sep 2020

Contributions:4 commits, 3 pushes, 1 branch in 3 years 4 months

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